1. Field of the Invention
The present invention relates to solution-data edit processing for editing solution data which is used in processing for automatically summarizing documents, articles and the like by a machine learning method, and to an automatic summarization processing which uses a machine learning method using editable solution data.
2. Description of the Related Art
In recent years, automatic summarization processing of documents, articles, etc. using a computer has become widespread with developments in information technologies. However, desired summary tendencies are considered to have been diversified by diversification of individual preferences and the summarization purposes.
Using the following Reference 1, personal differences of the summary evaluation tendencies will be described. In the Reference 1, when a plurality of evaluators perform summarization by extracting important sentences individually, a reproduction ratio and a relevance ratio are obtained as mutual evaluation measurement for the result and shown in Table 4. As is apparent from Table 4 in the Reference 1, in the case of summarization by extracting 20 sentences from a group of sentences, with respect to the mutual evaluation (a reproduction ratio and a relevance ratio) among the evaluators, coincidence degrees of each of the evaluators A, B, and C are from 50 to 70%, which are not too high. Thus it is estimated that personal differences exist in evaluation of summaries.
[Reference 1: Yamahiko Ito, et al., Extraction of important sentences from lecture sentences, Language Processing Society, the seventh annual convention proceeding (     7 ), 2001, pp. 305–308]
Also, in the following Reference 2, with reference to important sentence extraction processing, it is shown in Table 4 that the cross-verification precision for the processing sets A, B, and C is best. The target of the cross-verification shown in Table 4 in the Reference 2 can be regarded as the same as the processing by the same evaluator. It is unknown whether the sets A, B, and C in Table 4 is created by the same person. However, it is well understood that the processing precision will be favorable at least when the learning data is created at the same time or by the same person.
[Reference 2: Tsutomu Hirao, et al., Extraction of important sentences by Support Vector Machine, Information Society, basic papers (  Support Vector Machine  ), 63-16, 2001, pp. 121–127]
From the conventional research result, it is considered that personal differences and use differences exist in evaluation of summaries. In automatic summarization processing using a machine learning method, a summary specialized for an individual user needs to be created rather than performing summarization based on the same evaluation. In order to achieve this, in automatic summarization processing, a mechanism in which a user can freely edit the solution data to be the supervised data data in a machine learning method needs to be established.